For semantic segmentation, we first converted each human classification, i.e. all boxes by one user for an image, to a mask. Sometimes boxes for different patterns overlap. In this case, the mask is chosen to represent the value of the smaller box. Overall, the amount of overlapping boxes is small, however, so that the resulting error is most likely negligible. To create a segmentation model, we used the fastai Python library v1https://docs.fast.ai/. The network architecture has a U-Net [1]} structure with a Resnet50 backbone. For the segmentation model the images were downscaled to 700 by 466 pixels (batch size = 6).
